Currently, when data is collected, it is usually collected for a specific need or situation. This includes text and image data. When a new need or situation arises, the data collection process repeats, often without referencing the original data collected for previous situations. Data Transpositioning is a search methodology that leverages the context of a previous manual search process, to formulate a new automated search with new results. As a result, the data collection process for one situation, can quickly be applied to another situation, but with less user effort. Thus, a set of new results can quickly be constructed without the user manually revisiting each of the originating sources. In the case of Content-Based Image Retrieval, the idea is to identify the content attributes of an image, such as a particular color, shape or texture, and apply changes to the originating query, and return a new set of results with the similar attributes. Data Transpositioning has been successfully applied to the result sets that contain text. Our goal is to continue this research beyond text to solve more complex problems in other domains, especially when image data are involved.